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Updated: Jun 9, 2026
Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling
Published on: May 30, 2011
Pulmonary Transit Time Can Be Accurately Quantified From Non-Arterial Input Function Series in First-Pass Cardiac
Mingyue Zhao1,2, Lexiaozi Fan1, Kyungpyo Hong1
1Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Purpose:
Previous studies suggested that arterial-input-function (AIF) images are necessary to avoid signal saturation for pulmonary transit time (PTT) measurements. This study challenges that notion by investigating whether PTT can be accurately measured using blood pool signals from myocardial enhancement (non-AIF) images during resting first-pass perfusion MRI.
Methods:
This retrospective study included 108 patients, 47 scanned using a research radial perfusion sequence, and 61 scanned with a prototype Cartesian quantitative perfusion (qPerf) sequence with inline PTT calculation. An automated pipeline was developed to compute PTT measurements from blood pool signals extracted from both AIF and non-AIF images, as well as from gadolinium concentration curves of the AIF images, using three methods: peak-to-peak timing, area-under-the-curve (AUC) derived centroids, and curve-fitting derived centroids.
Results:
Peak-to-peak PTT showed low bias (0.10-0.56 s; [1.4%-7.4%] of the mean), wide limits of agreement (5.71-7.42 s; [74.5%-109.8%]). In contrast, centroid-based methods, using both AUC and curve-fitting approaches, consistently yielded low bias (< 0.49 s; < 6.3%), narrower limits of agreement (1.83-2.84 s; 25.2%-36.8%). These findings indicate that centroid methods offer more precise and reliable PTT estimation across both radial perfusion and qPerf datasets.
Conclusions:
PTT can be derived accurately from non-AIF images using either the AUC or curve-fitting centroid-to-centroid method.
